A modular neural network architecture for pattern classification
H. Elsherif, Mohamed L. Hambaba · 2002
A modular neural network architecture is proposed to classify binary and continuous patterns. This system consists of a supervised feedforward backpropagation network and an unsupervised self-organization map network. The supervised feedforward (basic) network is trained until a saturation error level occurs. Simultaneously, the unsupervised self-organization map (control) network fluids the mapping features for the given input/output patterns. The resultant features are used by Gaussian and linear functions to adjust the hidden and the output weights of the basic network and to classify the given patterns.>